This commit is contained in:
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tools/infer/predict_rec.py
Executable file
896
tools/infer/predict_rec.py
Executable file
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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from PIL import Image
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "../..")))
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os.environ["FLAGS_allocator_strategy"] = "auto_growth"
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import cv2
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import numpy as np
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import math
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import time
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import traceback
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import paddle
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import tools.infer.utility as utility
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from ppocr.postprocess import build_post_process
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from ppocr.utils.logging import get_logger
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from ppocr.utils.utility import get_image_file_list, check_and_read
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logger = get_logger()
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class TextRecognizer(object):
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def __init__(self, args, logger=None):
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if os.path.exists(f"{args.rec_model_dir}/inference.yml"):
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model_config = utility.load_config(f"{args.rec_model_dir}/inference.yml")
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model_name = model_config.get("Global", {}).get("model_name", "")
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if model_name and model_name not in [
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"PP-OCRv5_mobile_rec",
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"PP-OCRv5_server_rec",
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]:
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raise ValueError(
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f"{model_name} is not supported. Please check if the model is supported by the PaddleOCR wheel."
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)
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if args.rec_char_dict_path == "./ppocr/utils/ppocr_keys_v1.txt":
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rec_char_list = model_config.get("PostProcess", {}).get(
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"character_dict", []
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)
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if rec_char_list:
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new_rec_char_dict_path = f"{args.rec_model_dir}/ppocr_keys.txt"
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with open(new_rec_char_dict_path, "w", encoding="utf-8") as f:
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f.writelines([char + "\n" for char in rec_char_list])
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args.rec_char_dict_path = new_rec_char_dict_path
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if logger is None:
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logger = get_logger()
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self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
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self.rec_batch_num = args.rec_batch_num
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self.rec_algorithm = args.rec_algorithm
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postprocess_params = {
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"name": "CTCLabelDecode",
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char,
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}
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if self.rec_algorithm == "SRN":
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postprocess_params = {
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"name": "SRNLabelDecode",
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char,
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}
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elif self.rec_algorithm == "RARE":
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postprocess_params = {
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"name": "AttnLabelDecode",
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char,
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}
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elif self.rec_algorithm == "NRTR":
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postprocess_params = {
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"name": "NRTRLabelDecode",
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char,
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}
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elif self.rec_algorithm == "SAR":
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postprocess_params = {
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"name": "SARLabelDecode",
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char,
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}
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elif self.rec_algorithm == "VisionLAN":
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postprocess_params = {
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"name": "VLLabelDecode",
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char,
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"max_text_length": args.max_text_length,
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}
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elif self.rec_algorithm == "ViTSTR":
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postprocess_params = {
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"name": "ViTSTRLabelDecode",
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char,
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}
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elif self.rec_algorithm == "ABINet":
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postprocess_params = {
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"name": "ABINetLabelDecode",
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char,
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}
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elif self.rec_algorithm == "SPIN":
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postprocess_params = {
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"name": "SPINLabelDecode",
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char,
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}
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elif self.rec_algorithm == "RobustScanner":
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postprocess_params = {
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"name": "SARLabelDecode",
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char,
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"rm_symbol": True,
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}
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elif self.rec_algorithm == "RFL":
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postprocess_params = {
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"name": "RFLLabelDecode",
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"character_dict_path": None,
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"use_space_char": args.use_space_char,
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}
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elif self.rec_algorithm == "SATRN":
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postprocess_params = {
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"name": "SATRNLabelDecode",
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char,
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"rm_symbol": True,
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}
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elif self.rec_algorithm in ["CPPD", "CPPDPadding"]:
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postprocess_params = {
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"name": "CPPDLabelDecode",
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char,
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"rm_symbol": True,
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}
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elif self.rec_algorithm == "PREN":
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postprocess_params = {"name": "PRENLabelDecode"}
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elif self.rec_algorithm == "CAN":
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self.inverse = args.rec_image_inverse
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postprocess_params = {
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"name": "CANLabelDecode",
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char,
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}
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elif self.rec_algorithm == "LaTeXOCR":
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postprocess_params = {
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"name": "LaTeXOCRDecode",
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"rec_char_dict_path": args.rec_char_dict_path,
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}
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elif self.rec_algorithm == "ParseQ":
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postprocess_params = {
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"name": "ParseQLabelDecode",
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char,
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}
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self.postprocess_op = build_post_process(postprocess_params)
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self.postprocess_params = postprocess_params
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(
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self.predictor,
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self.input_tensor,
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self.output_tensors,
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self.config,
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) = utility.create_predictor(args, "rec", logger)
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self.benchmark = args.benchmark
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self.use_onnx = args.use_onnx
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if args.benchmark:
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import auto_log
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pid = os.getpid()
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gpu_id = utility.get_infer_gpuid()
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self.autolog = auto_log.AutoLogger(
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model_name="rec",
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model_precision=args.precision,
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batch_size=args.rec_batch_num,
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data_shape="dynamic",
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save_path=None, # not used if logger is not None
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inference_config=self.config,
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pids=pid,
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process_name=None,
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gpu_ids=gpu_id if args.use_gpu else None,
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time_keys=["preprocess_time", "inference_time", "postprocess_time"],
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warmup=0,
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logger=logger,
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)
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self.return_word_box = args.return_word_box
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def resize_norm_img(self, img, max_wh_ratio):
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imgC, imgH, imgW = self.rec_image_shape
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if self.rec_algorithm == "NRTR" or self.rec_algorithm == "ViTSTR":
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# return padding_im
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image_pil = Image.fromarray(np.uint8(img))
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if self.rec_algorithm == "ViTSTR":
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img = image_pil.resize([imgW, imgH], Image.BICUBIC)
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else:
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img = image_pil.resize([imgW, imgH], Image.Resampling.LANCZOS)
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img = np.array(img)
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norm_img = np.expand_dims(img, -1)
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norm_img = norm_img.transpose((2, 0, 1))
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if self.rec_algorithm == "ViTSTR":
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norm_img = norm_img.astype(np.float32) / 255.0
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else:
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norm_img = norm_img.astype(np.float32) / 128.0 - 1.0
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return norm_img
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elif self.rec_algorithm == "RFL":
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_CUBIC)
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resized_image = resized_image.astype("float32")
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resized_image = resized_image / 255
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resized_image = resized_image[np.newaxis, :]
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resized_image -= 0.5
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resized_image /= 0.5
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return resized_image
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assert imgC == img.shape[2]
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imgW = int((imgH * max_wh_ratio))
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if self.use_onnx:
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w = self.input_tensor.shape[3:][0]
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if isinstance(w, str):
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pass
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elif w is not None and w > 0:
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imgW = w
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h, w = img.shape[:2]
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ratio = w / float(h)
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if math.ceil(imgH * ratio) > imgW:
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resized_w = imgW
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else:
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resized_w = int(math.ceil(imgH * ratio))
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if self.rec_algorithm == "RARE":
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if resized_w > self.rec_image_shape[2]:
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resized_w = self.rec_image_shape[2]
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imgW = self.rec_image_shape[2]
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resized_image = cv2.resize(img, (resized_w, imgH))
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resized_image = resized_image.astype("float32")
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
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padding_im[:, :, 0:resized_w] = resized_image
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return padding_im
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def resize_norm_img_vl(self, img, image_shape):
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imgC, imgH, imgW = image_shape
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img = img[:, :, ::-1] # bgr2rgb
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resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
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resized_image = resized_image.astype("float32")
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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return resized_image
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def resize_norm_img_srn(self, img, image_shape):
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imgC, imgH, imgW = image_shape
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img_black = np.zeros((imgH, imgW))
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im_hei = img.shape[0]
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im_wid = img.shape[1]
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if im_wid <= im_hei * 1:
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img_new = cv2.resize(img, (imgH * 1, imgH))
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elif im_wid <= im_hei * 2:
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img_new = cv2.resize(img, (imgH * 2, imgH))
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elif im_wid <= im_hei * 3:
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img_new = cv2.resize(img, (imgH * 3, imgH))
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else:
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img_new = cv2.resize(img, (imgW, imgH))
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img_np = np.asarray(img_new)
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img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
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img_black[:, 0 : img_np.shape[1]] = img_np
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img_black = img_black[:, :, np.newaxis]
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row, col, c = img_black.shape
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c = 1
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return np.reshape(img_black, (c, row, col)).astype(np.float32)
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def srn_other_inputs(self, image_shape, num_heads, max_text_length):
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imgC, imgH, imgW = image_shape
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feature_dim = int((imgH / 8) * (imgW / 8))
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encoder_word_pos = (
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np.array(range(0, feature_dim)).reshape((feature_dim, 1)).astype("int64")
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)
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gsrm_word_pos = (
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np.array(range(0, max_text_length))
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.reshape((max_text_length, 1))
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.astype("int64")
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)
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gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
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gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
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[-1, 1, max_text_length, max_text_length]
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)
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gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1, [1, num_heads, 1, 1]).astype(
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"float32"
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) * [-1e9]
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gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
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[-1, 1, max_text_length, max_text_length]
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)
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gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2, [1, num_heads, 1, 1]).astype(
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"float32"
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) * [-1e9]
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encoder_word_pos = encoder_word_pos[np.newaxis, :]
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gsrm_word_pos = gsrm_word_pos[np.newaxis, :]
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return [
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encoder_word_pos,
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gsrm_word_pos,
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gsrm_slf_attn_bias1,
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gsrm_slf_attn_bias2,
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]
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def process_image_srn(self, img, image_shape, num_heads, max_text_length):
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norm_img = self.resize_norm_img_srn(img, image_shape)
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norm_img = norm_img[np.newaxis, :]
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[
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encoder_word_pos,
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gsrm_word_pos,
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gsrm_slf_attn_bias1,
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gsrm_slf_attn_bias2,
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] = self.srn_other_inputs(image_shape, num_heads, max_text_length)
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gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32)
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gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32)
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encoder_word_pos = encoder_word_pos.astype(np.int64)
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gsrm_word_pos = gsrm_word_pos.astype(np.int64)
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return (
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norm_img,
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encoder_word_pos,
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gsrm_word_pos,
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gsrm_slf_attn_bias1,
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gsrm_slf_attn_bias2,
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)
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def resize_norm_img_sar(self, img, image_shape, width_downsample_ratio=0.25):
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imgC, imgH, imgW_min, imgW_max = image_shape
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h = img.shape[0]
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w = img.shape[1]
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valid_ratio = 1.0
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# make sure new_width is an integral multiple of width_divisor.
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width_divisor = int(1 / width_downsample_ratio)
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# resize
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ratio = w / float(h)
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resize_w = math.ceil(imgH * ratio)
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if resize_w % width_divisor != 0:
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resize_w = round(resize_w / width_divisor) * width_divisor
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if imgW_min is not None:
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resize_w = max(imgW_min, resize_w)
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if imgW_max is not None:
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valid_ratio = min(1.0, 1.0 * resize_w / imgW_max)
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resize_w = min(imgW_max, resize_w)
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resized_image = cv2.resize(img, (resize_w, imgH))
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resized_image = resized_image.astype("float32")
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# norm
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if image_shape[0] == 1:
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resized_image = resized_image / 255
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resized_image = resized_image[np.newaxis, :]
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else:
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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resize_shape = resized_image.shape
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padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32)
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padding_im[:, :, 0:resize_w] = resized_image
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pad_shape = padding_im.shape
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return padding_im, resize_shape, pad_shape, valid_ratio
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def resize_norm_img_spin(self, img):
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# return padding_im
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img = cv2.resize(img, tuple([100, 32]), cv2.INTER_CUBIC)
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img = np.array(img, np.float32)
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img = np.expand_dims(img, -1)
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img = img.transpose((2, 0, 1))
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mean = [127.5]
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std = [127.5]
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mean = np.array(mean, dtype=np.float32)
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std = np.array(std, dtype=np.float32)
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mean = np.float32(mean.reshape(1, -1))
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stdinv = 1 / np.float32(std.reshape(1, -1))
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img -= mean
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img *= stdinv
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return img
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def resize_norm_img_svtr(self, img, image_shape):
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imgC, imgH, imgW = image_shape
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resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
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||||
resized_image = resized_image.astype("float32")
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||||
resized_image = resized_image.transpose((2, 0, 1)) / 255
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||||
resized_image -= 0.5
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||||
resized_image /= 0.5
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||||
return resized_image
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||||
|
||||
def resize_norm_img_cppd_padding(
|
||||
self, img, image_shape, padding=True, interpolation=cv2.INTER_LINEAR
|
||||
):
|
||||
imgC, imgH, imgW = image_shape
|
||||
h = img.shape[0]
|
||||
w = img.shape[1]
|
||||
if not padding:
|
||||
resized_image = cv2.resize(img, (imgW, imgH), interpolation=interpolation)
|
||||
resized_w = imgW
|
||||
else:
|
||||
ratio = w / float(h)
|
||||
if math.ceil(imgH * ratio) > imgW:
|
||||
resized_w = imgW
|
||||
else:
|
||||
resized_w = int(math.ceil(imgH * ratio))
|
||||
resized_image = cv2.resize(img, (resized_w, imgH))
|
||||
resized_image = resized_image.astype("float32")
|
||||
if image_shape[0] == 1:
|
||||
resized_image = resized_image / 255
|
||||
resized_image = resized_image[np.newaxis, :]
|
||||
else:
|
||||
resized_image = resized_image.transpose((2, 0, 1)) / 255
|
||||
resized_image -= 0.5
|
||||
resized_image /= 0.5
|
||||
padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
|
||||
padding_im[:, :, 0:resized_w] = resized_image
|
||||
|
||||
return padding_im
|
||||
|
||||
def resize_norm_img_abinet(self, img, image_shape):
|
||||
imgC, imgH, imgW = image_shape
|
||||
|
||||
resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
|
||||
resized_image = resized_image.astype("float32")
|
||||
resized_image = resized_image / 255.0
|
||||
|
||||
mean = np.array([0.485, 0.456, 0.406])
|
||||
std = np.array([0.229, 0.224, 0.225])
|
||||
resized_image = (resized_image - mean[None, None, ...]) / std[None, None, ...]
|
||||
resized_image = resized_image.transpose((2, 0, 1))
|
||||
resized_image = resized_image.astype("float32")
|
||||
|
||||
return resized_image
|
||||
|
||||
def norm_img_can(self, img, image_shape):
|
||||
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # CAN only predict gray scale image
|
||||
|
||||
if self.inverse:
|
||||
img = 255 - img
|
||||
|
||||
if self.rec_image_shape[0] == 1:
|
||||
h, w = img.shape
|
||||
_, imgH, imgW = self.rec_image_shape
|
||||
if h < imgH or w < imgW:
|
||||
padding_h = max(imgH - h, 0)
|
||||
padding_w = max(imgW - w, 0)
|
||||
img_padded = np.pad(
|
||||
img,
|
||||
((0, padding_h), (0, padding_w)),
|
||||
"constant",
|
||||
constant_values=(255),
|
||||
)
|
||||
img = img_padded
|
||||
|
||||
img = np.expand_dims(img, 0) / 255.0 # h,w,c -> c,h,w
|
||||
img = img.astype("float32")
|
||||
|
||||
return img
|
||||
|
||||
def pad_(self, img, divable=32):
|
||||
threshold = 128
|
||||
data = np.array(img.convert("LA"))
|
||||
if data[..., -1].var() == 0:
|
||||
data = (data[..., 0]).astype(np.uint8)
|
||||
else:
|
||||
data = (255 - data[..., -1]).astype(np.uint8)
|
||||
data = (data - data.min()) / (data.max() - data.min()) * 255
|
||||
if data.mean() > threshold:
|
||||
# To invert the text to white
|
||||
gray = 255 * (data < threshold).astype(np.uint8)
|
||||
else:
|
||||
gray = 255 * (data > threshold).astype(np.uint8)
|
||||
data = 255 - data
|
||||
|
||||
coords = cv2.findNonZero(gray) # Find all non-zero points (text)
|
||||
a, b, w, h = cv2.boundingRect(coords) # Find minimum spanning bounding box
|
||||
rect = data[b : b + h, a : a + w]
|
||||
im = Image.fromarray(rect).convert("L")
|
||||
dims = []
|
||||
for x in [w, h]:
|
||||
div, mod = divmod(x, divable)
|
||||
dims.append(divable * (div + (1 if mod > 0 else 0)))
|
||||
padded = Image.new("L", dims, 255)
|
||||
padded.paste(im, (0, 0, im.size[0], im.size[1]))
|
||||
return padded
|
||||
|
||||
def minmax_size_(
|
||||
self,
|
||||
img,
|
||||
max_dimensions,
|
||||
min_dimensions,
|
||||
):
|
||||
if max_dimensions is not None:
|
||||
ratios = [a / b for a, b in zip(img.size, max_dimensions)]
|
||||
if any([r > 1 for r in ratios]):
|
||||
size = np.array(img.size) // max(ratios)
|
||||
img = img.resize(tuple(size.astype(int)), Image.BILINEAR)
|
||||
if min_dimensions is not None:
|
||||
# hypothesis: there is a dim in img smaller than min_dimensions, and return a proper dim >= min_dimensions
|
||||
padded_size = [
|
||||
max(img_dim, min_dim)
|
||||
for img_dim, min_dim in zip(img.size, min_dimensions)
|
||||
]
|
||||
if padded_size != list(img.size): # assert hypothesis
|
||||
padded_im = Image.new("L", padded_size, 255)
|
||||
padded_im.paste(img, img.getbbox())
|
||||
img = padded_im
|
||||
return img
|
||||
|
||||
def norm_img_latexocr(self, img):
|
||||
# CAN only predict gray scale image
|
||||
shape = (1, 1, 3)
|
||||
mean = [0.7931, 0.7931, 0.7931]
|
||||
std = [0.1738, 0.1738, 0.1738]
|
||||
scale = np.float32(1.0 / 255.0)
|
||||
min_dimensions = [32, 32]
|
||||
max_dimensions = [672, 192]
|
||||
mean = np.array(mean).reshape(shape).astype("float32")
|
||||
std = np.array(std).reshape(shape).astype("float32")
|
||||
|
||||
im_h, im_w = img.shape[:2]
|
||||
if (
|
||||
min_dimensions[0] <= im_w <= max_dimensions[0]
|
||||
and min_dimensions[1] <= im_h <= max_dimensions[1]
|
||||
):
|
||||
pass
|
||||
else:
|
||||
img = Image.fromarray(np.uint8(img))
|
||||
img = self.minmax_size_(self.pad_(img), max_dimensions, min_dimensions)
|
||||
img = np.array(img)
|
||||
im_h, im_w = img.shape[:2]
|
||||
img = np.dstack([img, img, img])
|
||||
img = (img.astype("float32") * scale - mean) / std
|
||||
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||||
divide_h = math.ceil(im_h / 16) * 16
|
||||
divide_w = math.ceil(im_w / 16) * 16
|
||||
img = np.pad(
|
||||
img, ((0, divide_h - im_h), (0, divide_w - im_w)), constant_values=(1, 1)
|
||||
)
|
||||
img = img[:, :, np.newaxis].transpose(2, 0, 1)
|
||||
img = img.astype("float32")
|
||||
return img
|
||||
|
||||
def __call__(self, img_list):
|
||||
img_num = len(img_list)
|
||||
# Calculate the aspect ratio of all text bars
|
||||
width_list = []
|
||||
for img in img_list:
|
||||
width_list.append(img.shape[1] / float(img.shape[0]))
|
||||
# Sorting can speed up the recognition process
|
||||
indices = np.argsort(np.array(width_list))
|
||||
rec_res = [["", 0.0]] * img_num
|
||||
batch_num = self.rec_batch_num
|
||||
st = time.time()
|
||||
if self.benchmark:
|
||||
self.autolog.times.start()
|
||||
for beg_img_no in range(0, img_num, batch_num):
|
||||
end_img_no = min(img_num, beg_img_no + batch_num)
|
||||
norm_img_batch = []
|
||||
if self.rec_algorithm == "SRN":
|
||||
encoder_word_pos_list = []
|
||||
gsrm_word_pos_list = []
|
||||
gsrm_slf_attn_bias1_list = []
|
||||
gsrm_slf_attn_bias2_list = []
|
||||
if self.rec_algorithm == "SAR":
|
||||
valid_ratios = []
|
||||
imgC, imgH, imgW = self.rec_image_shape[:3]
|
||||
max_wh_ratio = imgW / imgH
|
||||
wh_ratio_list = []
|
||||
for ino in range(beg_img_no, end_img_no):
|
||||
h, w = img_list[indices[ino]].shape[0:2]
|
||||
wh_ratio = w * 1.0 / h
|
||||
max_wh_ratio = max(max_wh_ratio, wh_ratio)
|
||||
wh_ratio_list.append(wh_ratio)
|
||||
for ino in range(beg_img_no, end_img_no):
|
||||
if self.rec_algorithm == "SAR":
|
||||
norm_img, _, _, valid_ratio = self.resize_norm_img_sar(
|
||||
img_list[indices[ino]], self.rec_image_shape
|
||||
)
|
||||
norm_img = norm_img[np.newaxis, :]
|
||||
valid_ratio = np.expand_dims(valid_ratio, axis=0)
|
||||
valid_ratios.append(valid_ratio)
|
||||
norm_img_batch.append(norm_img)
|
||||
elif self.rec_algorithm == "SRN":
|
||||
norm_img = self.process_image_srn(
|
||||
img_list[indices[ino]], self.rec_image_shape, 8, 25
|
||||
)
|
||||
encoder_word_pos_list.append(norm_img[1])
|
||||
gsrm_word_pos_list.append(norm_img[2])
|
||||
gsrm_slf_attn_bias1_list.append(norm_img[3])
|
||||
gsrm_slf_attn_bias2_list.append(norm_img[4])
|
||||
norm_img_batch.append(norm_img[0])
|
||||
elif self.rec_algorithm in ["SVTR", "SATRN", "ParseQ", "CPPD"]:
|
||||
norm_img = self.resize_norm_img_svtr(
|
||||
img_list[indices[ino]], self.rec_image_shape
|
||||
)
|
||||
norm_img = norm_img[np.newaxis, :]
|
||||
norm_img_batch.append(norm_img)
|
||||
elif self.rec_algorithm in ["CPPDPadding"]:
|
||||
norm_img = self.resize_norm_img_cppd_padding(
|
||||
img_list[indices[ino]], self.rec_image_shape
|
||||
)
|
||||
norm_img = norm_img[np.newaxis, :]
|
||||
norm_img_batch.append(norm_img)
|
||||
elif self.rec_algorithm in ["VisionLAN", "PREN"]:
|
||||
norm_img = self.resize_norm_img_vl(
|
||||
img_list[indices[ino]], self.rec_image_shape
|
||||
)
|
||||
norm_img = norm_img[np.newaxis, :]
|
||||
norm_img_batch.append(norm_img)
|
||||
elif self.rec_algorithm == "SPIN":
|
||||
norm_img = self.resize_norm_img_spin(img_list[indices[ino]])
|
||||
norm_img = norm_img[np.newaxis, :]
|
||||
norm_img_batch.append(norm_img)
|
||||
elif self.rec_algorithm == "ABINet":
|
||||
norm_img = self.resize_norm_img_abinet(
|
||||
img_list[indices[ino]], self.rec_image_shape
|
||||
)
|
||||
norm_img = norm_img[np.newaxis, :]
|
||||
norm_img_batch.append(norm_img)
|
||||
elif self.rec_algorithm == "RobustScanner":
|
||||
norm_img, _, _, valid_ratio = self.resize_norm_img_sar(
|
||||
img_list[indices[ino]],
|
||||
self.rec_image_shape,
|
||||
width_downsample_ratio=0.25,
|
||||
)
|
||||
norm_img = norm_img[np.newaxis, :]
|
||||
valid_ratio = np.expand_dims(valid_ratio, axis=0)
|
||||
valid_ratios = []
|
||||
valid_ratios.append(valid_ratio)
|
||||
norm_img_batch.append(norm_img)
|
||||
word_positions_list = []
|
||||
word_positions = np.array(range(0, 40)).astype("int64")
|
||||
word_positions = np.expand_dims(word_positions, axis=0)
|
||||
word_positions_list.append(word_positions)
|
||||
elif self.rec_algorithm == "CAN":
|
||||
norm_img = self.norm_img_can(img_list[indices[ino]], max_wh_ratio)
|
||||
norm_img = norm_img[np.newaxis, :]
|
||||
norm_img_batch.append(norm_img)
|
||||
norm_image_mask = np.ones(norm_img.shape, dtype="float32")
|
||||
word_label = np.ones([1, 36], dtype="int64")
|
||||
norm_img_mask_batch = []
|
||||
word_label_list = []
|
||||
norm_img_mask_batch.append(norm_image_mask)
|
||||
word_label_list.append(word_label)
|
||||
elif self.rec_algorithm == "LaTeXOCR":
|
||||
norm_img = self.norm_img_latexocr(img_list[indices[ino]])
|
||||
norm_img = norm_img[np.newaxis, :]
|
||||
norm_img_batch.append(norm_img)
|
||||
else:
|
||||
norm_img = self.resize_norm_img(
|
||||
img_list[indices[ino]], max_wh_ratio
|
||||
)
|
||||
norm_img = norm_img[np.newaxis, :]
|
||||
norm_img_batch.append(norm_img)
|
||||
norm_img_batch = np.concatenate(norm_img_batch)
|
||||
norm_img_batch = norm_img_batch.copy()
|
||||
if self.benchmark:
|
||||
self.autolog.times.stamp()
|
||||
|
||||
if self.rec_algorithm == "SRN":
|
||||
encoder_word_pos_list = np.concatenate(encoder_word_pos_list)
|
||||
gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list)
|
||||
gsrm_slf_attn_bias1_list = np.concatenate(gsrm_slf_attn_bias1_list)
|
||||
gsrm_slf_attn_bias2_list = np.concatenate(gsrm_slf_attn_bias2_list)
|
||||
|
||||
inputs = [
|
||||
norm_img_batch,
|
||||
encoder_word_pos_list,
|
||||
gsrm_word_pos_list,
|
||||
gsrm_slf_attn_bias1_list,
|
||||
gsrm_slf_attn_bias2_list,
|
||||
]
|
||||
if self.use_onnx:
|
||||
input_dict = {}
|
||||
input_dict[self.input_tensor.name] = norm_img_batch
|
||||
outputs = self.predictor.run(self.output_tensors, input_dict)
|
||||
preds = {"predict": outputs[2]}
|
||||
else:
|
||||
input_names = self.predictor.get_input_names()
|
||||
for i in range(len(input_names)):
|
||||
input_tensor = self.predictor.get_input_handle(input_names[i])
|
||||
input_tensor.copy_from_cpu(inputs[i])
|
||||
self.predictor.run()
|
||||
outputs = []
|
||||
for output_tensor in self.output_tensors:
|
||||
output = output_tensor.copy_to_cpu()
|
||||
outputs.append(output)
|
||||
if self.benchmark:
|
||||
self.autolog.times.stamp()
|
||||
preds = {"predict": outputs[2]}
|
||||
elif self.rec_algorithm == "SAR":
|
||||
valid_ratios = np.concatenate(valid_ratios)
|
||||
inputs = [
|
||||
norm_img_batch,
|
||||
np.array([valid_ratios], dtype=np.float32).T,
|
||||
]
|
||||
if self.use_onnx:
|
||||
input_dict = {}
|
||||
input_dict[self.input_tensor.name] = norm_img_batch
|
||||
outputs = self.predictor.run(self.output_tensors, input_dict)
|
||||
preds = outputs[0]
|
||||
else:
|
||||
input_names = self.predictor.get_input_names()
|
||||
for i in range(len(input_names)):
|
||||
input_tensor = self.predictor.get_input_handle(input_names[i])
|
||||
input_tensor.copy_from_cpu(inputs[i])
|
||||
self.predictor.run()
|
||||
outputs = []
|
||||
for output_tensor in self.output_tensors:
|
||||
output = output_tensor.copy_to_cpu()
|
||||
outputs.append(output)
|
||||
if self.benchmark:
|
||||
self.autolog.times.stamp()
|
||||
preds = outputs[0]
|
||||
elif self.rec_algorithm == "RobustScanner":
|
||||
valid_ratios = np.concatenate(valid_ratios)
|
||||
word_positions_list = np.concatenate(word_positions_list)
|
||||
inputs = [norm_img_batch, valid_ratios, word_positions_list]
|
||||
|
||||
if self.use_onnx:
|
||||
input_dict = {}
|
||||
input_dict[self.input_tensor.name] = norm_img_batch
|
||||
outputs = self.predictor.run(self.output_tensors, input_dict)
|
||||
preds = outputs[0]
|
||||
else:
|
||||
input_names = self.predictor.get_input_names()
|
||||
for i in range(len(input_names)):
|
||||
input_tensor = self.predictor.get_input_handle(input_names[i])
|
||||
input_tensor.copy_from_cpu(inputs[i])
|
||||
self.predictor.run()
|
||||
outputs = []
|
||||
for output_tensor in self.output_tensors:
|
||||
output = output_tensor.copy_to_cpu()
|
||||
outputs.append(output)
|
||||
if self.benchmark:
|
||||
self.autolog.times.stamp()
|
||||
preds = outputs[0]
|
||||
elif self.rec_algorithm == "CAN":
|
||||
norm_img_mask_batch = np.concatenate(norm_img_mask_batch)
|
||||
word_label_list = np.concatenate(word_label_list)
|
||||
inputs = [norm_img_batch, norm_img_mask_batch, word_label_list]
|
||||
if self.use_onnx:
|
||||
input_dict = {}
|
||||
input_dict[self.input_tensor.name] = norm_img_batch
|
||||
outputs = self.predictor.run(self.output_tensors, input_dict)
|
||||
preds = outputs
|
||||
else:
|
||||
input_names = self.predictor.get_input_names()
|
||||
input_tensor = []
|
||||
for i in range(len(input_names)):
|
||||
input_tensor_i = self.predictor.get_input_handle(input_names[i])
|
||||
input_tensor_i.copy_from_cpu(inputs[i])
|
||||
input_tensor.append(input_tensor_i)
|
||||
self.input_tensor = input_tensor
|
||||
self.predictor.run()
|
||||
outputs = []
|
||||
for output_tensor in self.output_tensors:
|
||||
output = output_tensor.copy_to_cpu()
|
||||
outputs.append(output)
|
||||
if self.benchmark:
|
||||
self.autolog.times.stamp()
|
||||
preds = outputs
|
||||
elif self.rec_algorithm == "LaTeXOCR":
|
||||
inputs = [norm_img_batch]
|
||||
if self.use_onnx:
|
||||
input_dict = {}
|
||||
input_dict[self.input_tensor.name] = norm_img_batch
|
||||
outputs = self.predictor.run(self.output_tensors, input_dict)
|
||||
preds = outputs
|
||||
else:
|
||||
input_names = self.predictor.get_input_names()
|
||||
input_tensor = []
|
||||
for i in range(len(input_names)):
|
||||
input_tensor_i = self.predictor.get_input_handle(input_names[i])
|
||||
input_tensor_i.copy_from_cpu(inputs[i])
|
||||
input_tensor.append(input_tensor_i)
|
||||
self.input_tensor = input_tensor
|
||||
self.predictor.run()
|
||||
outputs = []
|
||||
for output_tensor in self.output_tensors:
|
||||
output = output_tensor.copy_to_cpu()
|
||||
outputs.append(output)
|
||||
if self.benchmark:
|
||||
self.autolog.times.stamp()
|
||||
preds = outputs
|
||||
else:
|
||||
if self.use_onnx:
|
||||
input_dict = {}
|
||||
input_dict[self.input_tensor.name] = norm_img_batch
|
||||
outputs = self.predictor.run(self.output_tensors, input_dict)
|
||||
preds = outputs[0]
|
||||
else:
|
||||
self.input_tensor.copy_from_cpu(norm_img_batch)
|
||||
self.predictor.run()
|
||||
outputs = []
|
||||
for output_tensor in self.output_tensors:
|
||||
output = output_tensor.copy_to_cpu()
|
||||
outputs.append(output)
|
||||
if self.benchmark:
|
||||
self.autolog.times.stamp()
|
||||
if len(outputs) != 1:
|
||||
preds = outputs
|
||||
else:
|
||||
preds = outputs[0]
|
||||
if self.postprocess_params["name"] == "CTCLabelDecode":
|
||||
rec_result = self.postprocess_op(
|
||||
preds,
|
||||
return_word_box=self.return_word_box,
|
||||
wh_ratio_list=wh_ratio_list,
|
||||
max_wh_ratio=max_wh_ratio,
|
||||
)
|
||||
elif self.postprocess_params["name"] == "LaTeXOCRDecode":
|
||||
preds = [p.reshape([-1]) for p in preds]
|
||||
rec_result = self.postprocess_op(preds)
|
||||
else:
|
||||
rec_result = self.postprocess_op(preds)
|
||||
for rno in range(len(rec_result)):
|
||||
rec_res[indices[beg_img_no + rno]] = rec_result[rno]
|
||||
if self.benchmark:
|
||||
self.autolog.times.end(stamp=True)
|
||||
return rec_res, time.time() - st
|
||||
|
||||
|
||||
def main(args):
|
||||
image_file_list = get_image_file_list(args.image_dir)
|
||||
valid_image_file_list = []
|
||||
img_list = []
|
||||
|
||||
# logger
|
||||
log_file = args.save_log_path
|
||||
if os.path.isdir(args.save_log_path) or (
|
||||
not os.path.exists(args.save_log_path) and args.save_log_path.endswith("/")
|
||||
):
|
||||
log_file = os.path.join(log_file, "benchmark_recognition.log")
|
||||
logger = get_logger(log_file=log_file)
|
||||
|
||||
# create text recognizer
|
||||
text_recognizer = TextRecognizer(args)
|
||||
|
||||
logger.info(
|
||||
"In PP-OCRv3, rec_image_shape parameter defaults to '3, 48, 320', "
|
||||
"if you are using recognition model with PP-OCRv2 or an older version, please set --rec_image_shape='3,32,320"
|
||||
)
|
||||
|
||||
# warmup 2 times
|
||||
if args.warmup:
|
||||
img = np.random.uniform(0, 255, [48, 320, 3]).astype(np.uint8)
|
||||
for i in range(2):
|
||||
res = text_recognizer([img] * int(args.rec_batch_num))
|
||||
|
||||
for image_file in image_file_list:
|
||||
img, flag, _ = check_and_read(image_file)
|
||||
if not flag:
|
||||
img = cv2.imread(image_file)
|
||||
if img is None:
|
||||
logger.info("error in loading image:{}".format(image_file))
|
||||
continue
|
||||
valid_image_file_list.append(image_file)
|
||||
img_list.append(img)
|
||||
try:
|
||||
rec_res, _ = text_recognizer(img_list)
|
||||
|
||||
except Exception as E:
|
||||
logger.info(traceback.format_exc())
|
||||
logger.info(E)
|
||||
exit()
|
||||
for ino in range(len(img_list)):
|
||||
logger.info(
|
||||
"Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[ino])
|
||||
)
|
||||
if args.benchmark:
|
||||
text_recognizer.autolog.report()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main(utility.parse_args())
|
||||
Reference in New Issue
Block a user